Self-learning connected-device network

ABSTRACT

A connected-device network can continually learn from abstract sensory data (e.g., speech processing, cognitive inference, and/or computer vision image segmentation) and can generate never-seen-before data in real time. In one aspect, the network devices extract important correlations in the sensor data based on network data collected at different time slice and/or locations. Further, underlying relationships in a set of data can be detected as the sensor data transverses through different layers of the network. Moreover, the network devices can provide logic in different layers to help classify the sensor data early in the detection process (e.g., instead of waiting for it to reach its final destination).

RELATED APPLICATION

The subject patent application is a continuation of, and claims priorityto, U.S. patent application Ser. No. 16/299,088, filed Mar. 11, 2019,and entitled “SELF-LEARNING CONNECTED-DEVICE NETWORK,” the entirety ofwhich application is hereby incorporated by reference herein.

TECHNICAL FIELD

The subject disclosure relates to wireless communications, e.g., systemsand methods that provide a self-learning connected-device network.

BACKGROUND

Internet of things (IoT) technology holds a great promise for the futureof the global communications industry. As the number of connecteddevices that can establish connectivity with other devices and/orpassive objects to exchange data continues to rise steadily, the IoTtechnology gains widespread proliferation in the information technologyindustry. With an anticipated projection of over 20 billion devices inthe next few years, service providers, network providers and/or cloudproviders will observe a net increase in their traffic handlingcapabilities. This can help the providers enable new IoT servicestailored to targeted industry verticals.

Typically, connected-device networks are implemented in dynamicenvironments that change rapidly over time. Log files stored in a cloudcapture the final states of the devices (e.g., sensors) at specificinstance. However, it can be challenging to keep track of who/whattriggered a state change in real time. Complex mathematical models thatutilize special skills and extensive human intervention are employed tofine tune and train the log data. This process can be significantly timeconsuming and inefficient.

The above-described background relating to mobility networks is merelyintended to provide a contextual overview of some current issues and isnot intended to be exhaustive. Other contextual information may becomefurther apparent upon review of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system that depicts a self-learningnetwork employed to process and/or classify Internet of things (IoT)data.

FIG. 2 illustrates an example system that employs a self-learningnetwork to process IoT data.

FIG. 3 illustrates an example system that comprises a fifth generation(5G) network cloud learning system.

FIG. 4 illustrates an example system comprising a sub-network of aself-learning network.

FIG. 5 illustrates an example system that provides a self-learningnetwork that is integrated with a non-3GPP network.

FIG. 6 illustrates an example system that facilitates automating one ormore features in accordance with the subject embodiments.

FIG. 7 illustrates an example method that provides a real-time andself-learning connected-device network.

FIG. 8 illustrates an example method for real-time classification of newsensor data via a self-learning network.

FIG. 9 illustrates an example system that depicts a service-based 5Gnetwork architecture.

FIG. 10 illustrates an example system that depicts a non-roaming 5Gsystem architecture in reference point representation.

FIG. 11 illustrates a block diagram of a computer operable to executethe disclosed communication architecture.

FIG. 12 illustrates a schematic block diagram of a computing environmentin accordance with the subject specification.

DETAILED DESCRIPTION

One or more embodiments are now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various embodiments. It may be evident,however, that the various embodiments can be practiced without thesespecific details, e.g., without applying to any particular networkedenvironment or standard. In other instances, well-known structures anddevices are shown in block diagram form in order to facilitatedescribing the embodiments in additional detail.

As used in this application, the terms “component,” “module,” “system,”“interface,” “node,” “platform,” “server,” “controller,” “entity,”“element,” “gateway,” or the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution or an entity related to anoperational machine with one or more specific functionalities. Forexample, a component may be, but is not limited to being, a processrunning on a processor, a processor, an object, an executable, a threadof execution, computer-executable instruction(s), a program, and/or acomputer. By way of illustration, both an application running on acontroller and the controller can be a component. One or more componentsmay reside within a process and/or thread of execution and a componentmay be localized on one computer and/or distributed between two or morecomputers. As another example, an interface can comprise input/output(I/O) components as well as associated processor, application, and/orAPI components.

Further, the various embodiments can be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement one or moreaspects of the disclosed subject matter. An article of manufacture canencompass a computer program accessible from any computer-readabledevice or computer-readable storage/communications media. For example,computer readable storage media can comprise but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical disks (e.g., compact disk (CD), digital versatile disk(DVD) . . . ), smart cards, and flash memory devices (e.g., card, stick,key drive . . . ). Of course, those skilled in the art will recognizemany modifications can be made to this configuration without departingfrom the scope or spirit of the various embodiments.

In addition, the word “example” or “exemplary” is used herein to meanserving as an example, instance, or illustration. As used in thisapplication, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

Terms like “user equipment” or similar terminology, refer to a wired orwireless communication-capable device utilized by a subscriber or userof a wired or wireless communication service to receive or convey data,control, voice, video, sound, gaming, or substantially any data-streamor signaling-stream. Data and signaling streams can be packetized orframe-based flows. Further, the terms “user,” “subscriber,” “consumer,”“customer,” and the like are employed interchangeably throughout thesubject specification, unless context warrants particular distinction(s)among the terms. It should be noted that such terms can refer to humanentities or automated components supported through artificialintelligence (e.g., a capacity to make inference based on complexmathematical formalisms), which can provide simulated vision, soundrecognition and so forth.

Furthermore, it is noted that the term “cloud” as used herein can referto a set of servers, communicatively and/or operatively coupled to eachother, that host a set of applications utilized for servicing userrequests. In general, the cloud computing resources can communicate withuser devices via most any wired and/or wireless communication network toprovide access to services that are based in the cloud and not storedlocally (e.g., on the user device). A typical cloud computingenvironment can include multiple layers, aggregated together, thatinteract with each other to provide resources for end-users.

Aspects or features of the disclosed subject matter can be exploited insubstantially any wired or wireless communication technology; e.g.,universal mobile telecommunications system (UMTS), Wi-Fi, worldwideinteroperability for microwave access (WiMAX), general packet radioservice (GPRS), enhanced GPRS, third generation partnership project(3GPP) long term evolution (LTE), fifth generation (5G) or other nextgeneration networks, third generation partnership project 2 (3GPP2)ultra mobile broadband (UMB), high speed packet access (HSPA), Zigbee,or another IEEE 802.XX technology, low power wide area (LPWA) and/ornon-3GPP standard based solutions, such as, but not limited to, Ingenu,Sigfox, and/or LoRa, etc. Additionally, substantially all aspects of thedisclosed subject matter can be exploited in legacy (e.g., wireline)telecommunication technologies.

Internet of things (IoT), which is the future of internet connectivity,enables creation of an information rich ecosystem that can enrich modernconnected way of life and transform the way in which businesses as wellas consumers function today. Typically, IoT/machine-to-machine (M2M)devices have different characteristics (e.g., communicationcharacteristics, signaling characteristics, operating characteristics,etc.) than regular/commercial user equipment (UEs) (e.g., non-IoTdevices, such as, but not limited to, smart phones, tablet computers,personal computers, etc.). For example, the IoT/M2M devices collectivelygenerate a much greater number of signaling connections in the mobilecore network as compared to regular UEs. Further, in another example,the service/application provider often performs simultaneous devicetriggering and monitoring for targeted IoT applications and services. Inaddition, the IoT/M2M devices operate in a low-power/sleep mode forlonger durations (e.g., 99% of the time) as compared to conventionalnon-IoT devices.

As a variety of IoT device categories emerge based on 3GPP standardsevolution supporting a multitude of services, there is an increasingdemand on the various network functions within the mobilityinfrastructure to be more intelligent, dynamic, adaptive, and flexiblewith their interworking to provide the best possible node levelfunctions and end-to-end service behaviors. The systems and methodsdisclosed herein can provide a network that continually learns fromabstract sensory data (e.g., not just 0s and 1s) and is capable ofgenerating never seen before data (e.g., correlations, patterns,classifications, etc.). Conventional machine learning frameworks utilizeexplicit programming or pre-defined models/algorithms that are appliedto data logs (e.g., representing a final state of sensor data) within acustomer cloud. Moreover, these frameworks require customers to storetheir data logs in the cloud and “train” the data logs by applyingdifferent models. For dynamic environments that can change rapidly overtime, these models cannot provide real-time tracking data. In contrast,the systems and methods disclosed herein enable network operators (e.g.,mobile network operators (MNOs)) to extract important correlations inthe sensor data based on network data collected at different time sliceand/or locations. In one aspect, underlying relationships in a set ofdata can be detected as the sensor data transverses the network.Moreover, the network devices can provide logic in different layers tohelp classify the sensor data early in the process (e.g., instead ofwaiting for it to reach its final destination). This approach can handlesensor data that is more abstract, like speech processing, cognitiveinference, and/or computer vision image segmentation.

Referring initially to FIG. 1, there illustrated is an example system100 that depicts a self-learning network employed to process and/orclassify IoT data, according to one or more aspects of the disclosedsubject matter. Typically, system 100 can provide an efficient and fastapproach to deliver IoT services, for example, for various IoTapplications, such as, but not limited to, connected cars, smart cities,smart manufacturing and/or industrial automation, energy, security,FirstNet, healthcare, etc. In one example, the IoT services can enableoperations, such as, but not limited to, monitoring sensor data,creating alerts and/or notifying appropriate personnel, controllingand/or managing tasks (e.g., that can be performed by an IoT device, IoTgateway and/or other connected controller devices), etc.

In an aspect, a localized self-learning network 102 can be utilized totrack events (e.g., customized for specific customers) based on IoT data104. As an example, IoT data 104 can comprise raw sensor data generatedby one or more IoT devices. Typically, the IoT devices generate massiveamounts of data (e.g., abstract data and/or new data that has not beenpreviously processed) that can be processed in real time by thelocalized self-learning network 102 to convert it into information,correlations, and/or knowledge which is of interest to customers (e.g.,from which customers can gain insights and/or base decisions). This isdifferent from conventional explicit programming or pre-defined modelsthat require customers to transfer their data (e.g., logs representingfinal sensor states) in a customer cloud and “train” the data byapplying the different models. Network operators (e.g., MNOs) haveaccess to large amount of network intelligence data 106 collected atdifferent time slice and locations, which can be utilized to extractimportant correlations in the sensor data and generate output data 108in real time. As an example, the network intelligence data 106 cancomprise historical data collected during a defined time period and/orwithin defined geographical regions. Accordingly, the localizedself-learning network 102 can determine a localized policy/rule that isappropriate for sensor data collected within a specific area and utilizehistorically collected network information that is learned and storedwithin the “layered” network 102 to generate output data 108. In oneaspect, the localized self-learning network 102 can comprise multiplelayers with pre-defined “object nodes” that can be customized accordingto a customer's requirements/preferences. As an example, output data 108can be a classification or category, or an object node of the network102.

In one aspect, if new data (e.g., belonging to an unknown or newcategory) is discovered that cannot be classified by the existing objectnodes and/or has not been previously classified, the localizedself-learning network 102 can dynamically instantiate a new object nodewith an appropriate “label” or “category” in a secure manner. In oneaspect, subsequently received sensor data that has similarcharacteristics can be directed to the new object node. Further, thelocalized self-learning network 102 can employ a feedback mechanism tocapture adjustments via implicit and/or explicit methods. It is notedthat the detection of changes/events/patterns/correlations is in realtime rather than in the final state. In one aspect, the output data 108can be provided to an output engine for generating appropriate alertsand/or tasks.

According to an aspect, system 100 can be integrated with nextgeneration communication networks, for example, a 5G network, asresources can be instantiated dynamically, and wireless sensor data canbe captured in real time. However, it is noted that system 100 can alsobe used in conjunction with the existing traditional cloud networks. AsIoT data 104 is captured from the sensors (e.g., wired or wireless),system 100 can be implemented in the cloud without the need to train thedata and apply the predefined models to validate the data. Instead, thelocalized self-learning network 102 of system 100 can automaticallygenerate one or more object nodes that are appropriate for the sensordata in real time as the IoT data 104 is captured and/or received.

Referring now to FIG. 2, there illustrated is an example system 200 thatemploys a self-learning network to process IoT data, in accordance withan aspect of the subject disclosure. System 200 provides an efficientand quick approach to extract intelligence from IoT data and process theextracted intelligence in real time. Moreover, network virtualizationtechniques can be leveraged to provide a real-time and self-learningnetwork 102 for connected-devices (e.g., IoT devices). Typically, IoTdevices (e.g., sensors S1-SM 202 ₁-202 _(M); where M is most anyinteger) can provide abstract data, such as, but not limited to, imagesand/or video captured by the sensors 202 ₁-202 _(M). If a customer wantsto verify certain conditions and/or detect specific events (e.g., basedon video/image data), conventionally, human intervention is required toreview sensed data. Human intervention is performed within thecustomer's backend cloud network (not shown) and thus, the verificationand/or detection process can be extremely time consuming andinefficient. In contrast with the conventional approach, system 200employs a self-learning network 102 (e.g., front-end network) that cancollect, monitor, and classify data provided by the sensors 202 ₁-202_(M) in real time to determine criterion, such as, but not limited to,product trends, traffic forecasts, error conditions, fault detection,alerts, user behavior patterns, etc., which can be provided to thecustomer. In an aspect, the self-learning network 102 can extractenvironment data and learn from that, such that, when new data isreceived (e.g., previously unseen, unclassified, etc.), the network canclassify the new data and extract information determined to be ofimportance for customer. It is noted that the self-learning network 102can comprise functionality as more fully described herein, for example,as described above with regard to system 100.

According to an embodiment, when an IoT device (e.g., sensors 202 ₁-202_(M)) attaches to a wireless network (e.g., self-learning network 102),a service slice can be instantiated for a specific IoT service and/orspecific customer, such that appropriate security measures and/orcustomized features can be implemented within the slice. Collector nodesCl-CM 204 ₁-204 _(M) can collect metadata for respective IoT devices(e.g., sensors 202 ₁-202 _(M)). As an example, the metadata cancomprise, but is not limited to speed, time, device location, lastsession seen, communication protocol used, destination, device states(e.g., power on/idle/power off), etc. In an aspect, a collector node(e.g., Cl-CM 204 ₁-204 _(M)) can create a temporary identifier (ID) foreach IoT device to keep track of its metadata collected over time.Further, the collector node (e.g., Cl-CM 204 ₁-204 _(M)) can assign agroup identifier (ID) to a group of one or more sensors associated witha business contract. Additional metadata and/or attributes associatedwith the IoT device (e.g., sensors 202 ₁-202 _(M)) can be identified(e.g., via Cl-CM 204 ₁-204 _(M)) as the sensor data traverses throughone or more different network nodes. In one example, private attributescan be identified, and a security key(s) can be implemented in thecollector node to decrypt the received data.

In one aspect, sensor data captured via the IoT device can be stored(e.g., temporarily) within a data store of the self-learning network 102and values from one or more previous sessions (e.g., associated with thetemporary ID and/or group ID), contextual data (e.g. time/location forlast network connection), and/or historical data can be utilized (e.g.,by one or more network devices) to determine and/or recognize one ormore patterns within the sensor data. Additionally, network intelligencedata (e.g., monitored and/or collected by the network devices)comprising, but not limited to, normal latency pattern, uplink downlinkpattern, system information blocks (SIB) data, session information,historical data collected for a particular device category supported bythe network, area specific information, event data, etc., can beutilized for classification and/or determination of patterns.

The sensor data can be further validated using “object nodes” (N1-N16)to fine tune a “labeling” and/or other classification process. As anexample, the labeling process can be executed simultaneously (orsubstantially simultaneously) with multiple object nodes. In an aspect,an object node can be configured to modify its subsequent nodes tocreate logic and layers that will further identify the features andlabels of the sensor data. If a customer-desired output is detected, anoutput engine 206 can be triggered to alert the results. In one example,the results are exposed through one or more application programminginterfaces (APIs) that can be integrated into customer applications. Insome example scenarios, the results can be provided back to the IoTdevices (e.g., instructions to perform a task, control and/or manage theIoT device, etc.). In other example scenarios, the results can beprovided to a network operations support system (OSS) to facilitateoutage and/or congestion prevention. Additionally, a feedback component208 can utilize the output data 108 to improve (e.g., increaseclassification accuracy) the object nodes N1-N16. Moreover, optimizationis performed within the network without communicating with the Internetfor training/testing. It is noted that although system 200 depictssixteen object nodes, the subject specification is not that limited andthe self-learning network 102 can comprise fewer or greater number ofobject nodes.

Referring now to FIG. 3, there illustrated is an example system 300 thatcomprises a 5G network cloud learning system, in accordance with anaspect of the subject disclosure. In this embodiment, the self-learningnetwork 102 can be built on cloud technology that is based on networkfunctions virtualization (NFV) and/or software-defined networking (SDN).NFV can virtualize network services that have been conventionallycarried out by proprietary, dedicated hardware/software and instead hostthe network services on one or more virtual machines (VMs). Using NFV,network service providers do not need to purchase proprietary/dedicatedhardware devices to enable a service. NFV can improve scalability andflexibility and network capacity can easily be adjusted throughsoftware, resulting in reduced capital expenses and/or operatingexpenses. NFV and SDN are different technologies but complementary. SDNarchitectures decouple or disassociate network control (e.g., controlplane) and forwarding (e.g., data plane) functions. This allows fordynamic, programmable, and/or scalable computing and storage. The SDNarchitecture can be at least (i) directly programmable; (ii) agile;(iii) centrally managed; (iv) programmatically configured; and/or (v)open standards-based and vendor-neutral.

In one example, the mobility network 302 of system 300 can comprise 5Gand/or other next generation networks that provide enhanced mobilebroadband. In one aspect, the network functions that will serve 5Gand/or other next-generation technologies (e.g., network 302) can be“sliced” and be instantiated in any suitable edge office locations,besides central offices, on demand. Network slicing can transform amonolithic mobility networking architecture that has traditionally beenused to service smartphones in the current wireless network providerindustry. Generally, a slice (e.g., virtualized network functions (VNFs)304 ₁-304 ₄) can be a virtualization of a physical network that enablesindependent architecture, partitioning, and organization of computingresources in each slice. Moreover, network slices are a specific form ofvirtualization that allow multiple logical networks to run on top of ashared physical network infrastructure. This can facilitate flexibilitythat is typically not readily available in a monolithic embodiment of aphysical network. Network slicing can create logically separate slicesof the core network entities running on common mobility infrastructure,wherein each slice can provide customized connectivity for a service(and/or class of service). Typically, a slice, e.g., VNFs 304 ₁-304 ₄,can be considered self-contained with regard to operation, traffic flow,performance, etc., and can have its own virtualized architecture andfeatures, and can be individually provisioned in a network. Thevirtualization of physical network resources via slicing can simplifycreation, management, and operation of slices, typically tailored to atype of functionality, environment, service, hardware, etc., to enableefficient consumption of network resources of the physical network. AnSDN orchestration and control component 306 can manage and coordinatethe NVF slicing.

Sensors of IoT devices (e.g., 202 ₁-202 ₄) can feed sensor data (e.g.,audio data, images, videos, etc.) to a localized self-learning network102 for dynamic intelligent advice or action. In one example, the IoTdevices (e.g., 202 ₁-202 ₄) can provide the data directly to thecollector nodes (204 ₁-204 _(M)), via sensor pools 308. In anotherexample, the IoT devices (e.g., 202 ₁-202 ₄) can provide the data viaone or more access point devices (e.g., 310 ₁-310 ₂) of network 302.Moreover, the IoT devices (e.g., 202 ₁-202 ₄) can comprise most anyconnected device, such as, but not limited to, most any LTE-basedappliance, machine, device, security system, home automation system,satellite systems, automated vehicle and/or at least partially automatedvehicle (e.g., drones), etc. Further, the IoT devices (e.g., 202 ₁-202₄) can comprise one or more sensors and/or a radio-frequencyidentification (RFID) reader and can be typically employed for automateddata transmission and/or measurement between mechanical and/orelectronic devices. As discussed in detail with respect to system 200,the self-learning network 102 can configure multiple layers of objectnodes 312 (e.g., N1-N16) to determine output data that can be ofinterest to a customer (e.g., likelihood of the output data being ofinterest to the customer satisfies a defined criterion).

Additionally, taking the advantage of slicing models, an internallearning network layer or sub-network can leverage network information314, such as, but not limited to, knowledge base, operational logs,traffic logs, analytics elements, previously collectedinformation/historical data, application patterns, etc., created on VNFslices for a deeper analysis and learning on a more focused area and/orcategory, for example, type of device (e.g., connected car category, IoTcategory, mobile virtual network operator (MVNO) category, enterprisecategory, etc.). New VNFs can be dynamically instantiated based onlearning indications (e.g., determined by the output engine 206 theself-learning network 102). In one aspect, system 300 can comprise ahierarchy of self-learning networks 102, wherein training for eachself-learning network can be conducted recursively, forward and/orbackward propagating. Further, outputs from each self-learning networkcan also be fed to the next learning network or a sub-network (e.g.,network 302). Cycles of training can bring the network to a smarterlevel. Furthermore, the sub-network (e.g., network 302) can sharelearning information between slices (e.g., VNFs 304 ₁-304 ₄). It isnoted that although learning information (e.g., data that can beemployed to facilitate faster and/or accurate classification and/orevent detection) is shared between slices, private data and/or customerdata is not shared. Accordingly, the system 300 can be a multi layerlearning system, wherein each learning layer can be vertical andhorizontal. For example, if detected that the IoT device belongs to adefined type of device category, the output data 108 can be directed toa specific slice of network 302 for further analysis using networkinformation 314.

Referring now to FIG. 4, there illustrated is an example system 400comprising an example sub-network of a self-learning network, accordingto an aspect of the subject disclosure. Moreover, the sub-network canfacilitate vendor/customer-specific network slicing. In this exampleembodiment, the IoT devices (e.g., 2025-2028) can belong to the samedevice category, for example, connected cars, and the sub-network 302can instantiate slices (e.g., VNFs 402 ₁-402 ₄) that are customized forthe make, model, and/or manufacturer of the device. For example, VNF 402₁ can be instantiated to process data associated with connected cars ofCar original equipment manufacturer (OEM) #1, VNF 402 ₂ can beinstantiated to process data associated with connected cars of Car OEM#2, VNF 402 ₃ can be instantiated to process data associated withconnected cars of Car OEM #3, and VNF 402 ₄ can be instantiated toprocess data associated with connected cars of Car OEM #4. It is notedthat although system 400 depicts only four VNFs, a fewer or greaternumber of VNFs can be implemented. In one aspect, if the self-learningnetwork 102 detect that a connected car that does not belong to a make,model, variant, and/or manufacturer associated with the instantiatedVNFs 402 ₁-402 ₄, a new VNF can be dynamically instantiated for the newmake, model, variant, and/or manufacturer and data received fromconnected cars of the new make, model, variant, and/or manufacturer canbe directed to and/or processed via the new VNF.

FIG. 5 illustrates an example system 500 that provides a self-learningnetwork integrated with a non-3GPP network, according to an aspect ofthe subject disclosure. As an example, the non-3GPP cloud 502 cancomprise most any public cloud network. In one aspect, devices of thenon-3GPP cloud 502 can further process output data 108 to extractinformation that is likely to be of interest to the customer (e.g.,events, errors, failures, abnormal and/or irregular behavior, etc.).

Referring now to FIG. 6, there illustrated is an example system 600 thatemploys an artificial intelligence (AI) component (602) to facilitateautomating one or more features in accordance with the subjectembodiments. It can be noted that the self-learning network 102, outputengine 206, and feedback component 208 can comprise functionality asmore fully described herein, for example, as described above with regardto systems 100-500. In one aspect, data reception component 604 (e.g.,comprising collector nodes) can obtain information, such as, but notlimited to, sensor data, metadata associated with IoT device, networkintelligence data, etc. Based on an analysis of the information, a nodeconfiguration component 606 can configure object nodes 312 thatfacilitate classification of the sensor data to extract information thatis likely to be of interest and/or value to a customer.

In an example embodiment, system 600 (e.g., in connection with dataextraction) can employ various AI-based schemes (e.g., intelligentprocessing/analysis, machine learning, etc.) for carrying out variousaspects thereof. For example, a process for analyzing sensor data can befacilitated via an automatic classifier system implemented by AIcomponent 602. Moreover, the AI component 602 can exploit variousartificial intelligence (AI) methods or machine learning methods.Artificial intelligence techniques can typically apply advancedmathematical analysis—e.g., decision trees, neural networks, regressionanalysis, principal component analysis (PCA) for feature and patternextraction, cluster analysis, genetic algorithm, or reinforcedlearning—to a data set. In particular, AI component 602 can employ oneof numerous methodologies for learning from data and then drawinginferences from the models so constructed. For example, hidden markovmodels (HMMs) and related prototypical dependency models can beemployed. General probabilistic graphical models, such asDempster-Shafer networks and Bayesian networks like those created bystructure search using a Bayesian model score or approximation can alsobe utilized. In addition, linear classifiers, such as support vectormachines (SVMs), non-linear classifiers like methods referred to as“neural network” methodologies, fuzzy logic methodologies can also beemployed.

As will be readily appreciated from the subject specification, anexample embodiment can employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing device/operator preferences, historical information,receiving extrinsic information, type of service, type of device, etc.).For example, SVMs can be configured via a learning or training phasewithin a classifier constructor and feature selection module. Thus, theclassifier(s) of AI component 602 can be used to automatically learn andperform a number of functions, comprising but not limited to determiningaccording to a predetermined criteria, when and which actions are to beperformed (e.g., controlling functions of one or more IoT devices),notifications provided to a customer, determining information that islikely to be of interest to the customer, changes in patterns, trends,and/or device/user behavior, etc. The criteria can comprise, but is notlimited to, historical patterns and/or trends, network operatorpreferences and/or policies, customer preferences, predicted trafficflows, event data, latency data, reliability/availability data, currenttime/date, sensor data, weather data, type of IoT device, news, and thelike.

FIGS. 7-8 illustrate flow diagrams and/or methods in accordance with thedisclosed subject matter. For simplicity of explanation, the flowdiagrams and/or methods are depicted and described as a series of acts.It is to be understood and noted that the various embodiments are notlimited by the acts illustrated and/or by the order of acts, for exampleacts can occur in various orders and/or concurrently, and with otheracts not presented and described herein. Furthermore, not allillustrated acts may be required to implement the flow diagrams and/ormethods in accordance with the disclosed subject matter. In addition,those skilled in the art will understand and note that the methods couldalternatively be represented as a series of interrelated states via astate diagram or events. Additionally, it should be further noted thatthe methods disclosed hereinafter and throughout this specification arecapable of being stored on an article of manufacture to facilitatetransporting and transferring such methods to computers. The termarticle of manufacture, as used herein, is intended to encompass acomputer program accessible from any computer-readable device orcomputer-readable storage/communications media.

Referring now to FIG. 7 there illustrated is an example method 700 thatprovides a real-time and self-learning connected-device network,according to an aspect of the subject disclosure. In an aspect, method700 can be implemented by one or more network devices of a communicationnetwork (e.g., mobility network). Method 700 provides an efficientand/or low-latency approach to extract intelligence (e.g., informationlikely to be desired by a customer, likely to be of interest to acustomer, etc.) from received IoT data and be able to process it in realtime. Accordingly, the customer does not have to wait until the veryend, when IoT data is in the customer cloud to process it and detectevents.

At 702, it can be determined that an IoT device is coupled to theself-learning network (e.g., via a mobility network access point and/orsensor pool devices). At 704, metadata associated with the IoT devicecan be received. As an example, the metadata can comprise informationsuch as, but not limited to, device speed, time, device location, lastsession seen, communication protocol used, destination, device states,and/or any other attributes that can be received as the IoT device movesthrough different access networks (e.g., of the mobility network). Themetadata can be stored within a data store of the self-learning networkand at 706, IDs can be assigned to the metadata. For example, the IDscan comprise a device ID specific to the IoT device and/or a group IDfor a group of devices associated with a specific customeraccount/business contract. In one aspect, the metadata can compriseprivate attributes that can be decrypted based on one or more securitykeys implemented in a node (e.g., collector node) of the self-learningnetwork.

At 708, sensor data can be received from the IoT device. As an example,sensor data can comprise content (e.g., images, video, audio, and/ormeasurements) captured by the IoT device. Moreover, the sensor data canbe abstract, for example, not just 0s and 1s. At 710, information ofinterest (e.g., explicitly request by a customer and/or predicted to beof interest to a customer) can be extracted from the sensor data basedon an analysis of the metadata, context data (e.g., location, weather,news, time, etc.), network intelligence data (e.g., observed/learned bynetwork devices over time), etc. Further, the sensor data is validatedby employing object nodes to tune a labeling process. As an example, theobject nodes can be pre-defined for a customer and/or dynamicallyinstantiated based on the sensor data. Typically, the object nodes canbe layered, such that object nodes in a subsequent layer furtherclassify the sensor data (e.g., like peeling layers) to finally arriveat final classification (e.g., most accurate prediction). For example,in a shipping application, based on images/videos of ships, theself-learning network can determine information, such as, but notlimited to, whether the ship is a trade ship, the features of the cargo,any trade violations, etc. Accordingly, the network devices in thefront-end can perform the determination/classification and provideresults to a customer. Moreover, determination/classification isperformed in real time as sensor data is received rather than in a finalstate (e.g., logs stored in customer cloud).

At 714, in response to detecting the information of interest (e.g.,final classification), a rules engine can be triggered. In one aspect,the rules engine can apply pre-defined (e.g., by the customer and/ornetwork operator) and/or dynamically-determined policies to furtherprocess and/or the information. For example, final classificationresults can be exposed through APIs that are integrated into thecustomer applications. In one aspect, the final classification resultscan trigger tasks that are to be performed, for example, by the IoTdevice and/or other devices. For example, the sensor data can compriseimages of limestone rocks within a quarry, the self-learning network candetermine that calcium content of the rocks is at a certain level, andaccordingly, a rules engine can instruct a driver (e.g., via anapplication) to collect the specified rocks. In another example, thefinal classification results can provide results to OSS system toprevent outage/congestion in specific areas and/or during specificevents/time periods. At 716, feedback data (e.g., received customer/userinteraction) associated with the results can be utilized to improve theclassification and/or labeling process of one or more object nodes.Accordingly, the classification and/or labeling process is optimizedwithin the front-end network independent of accessing the Internet fortraining/testing.

FIG. 8 illustrates an example method 800 for real-time classification ofnew sensor data via a self-learning network, according to an aspect ofthe subject disclosure. As an example, method 800 can be implemented oneor more network devices of a communication network (e.g., cellularnetwork). To recognize underlying relationships in a set of sensor data,the self-learning network can help break down from a complicatedquestion—e.g., does this sensor data contains important information thatthe customer is looking for—into very simple questions at differentlayer of the network by employing layers of object nodes and traversingthe sensor data through a set of the object nodes.

At 802, new sensor data can be received. As an example, “new” sensordata can comprise information and/or categories that have previously notbeen encountered/classified by the network. On receiving such sensordata, at 804, a new object node with an appropriate/new “label” and/or“category” can be dynamically instantiated (e.g., via node configurationcomponent 606). Additionally or optionally, sub-network can dynamicallyinstantiate new slices (e.g., vendor-specific, device-specific,industry-specific, etc.). At 806, the sensor data can be validated byemploying the new object node. Further, at 808, in response to detectingan outcome that is likely to be of value to a customer, a rules enginecan be triggered to output the results (e.g., notify the customer,perform defined tasks, instruct defined devices, etc.)

Aspects and embodiments disclosed herein can be implemented in nextgeneration networks, for example, 5G networks. 5G are configured toprovide enhanced mobile broadband, for example, ultra high bandwidth(e.g., 20 Gbps), high spectral efficiency (e.g., 3.5× of LTE), ultradense networks, and/or energy efficiency. Further, the 5G networks canprovide ultra-reliable (e.g., high reliability greater than 99.999%) andlow latency communications (e.g., ultra low latency of ˜1 msec and/orlow network access and synchronization time). Furthermore, the 5Gnetworks can facilitate massive machine type communication (e.g., ultrahigh density (10⁶/sq km), long battery life (10 years+), high systemgain (better than narrow band-IoT and/or more efficient than narrowband-IoT).

The 5G network architecture is defined as service-based and theinteraction between network functions can be represented as shown inFIGS. 9-10. FIG. 9 illustrates an example system 900 that depicts aservice-based network architecture, according to an aspect of thesubject disclosure. In an aspect, system 900 depicts service-basedinterfaces being used within the control plane. For example, one networkfunction (e.g., AMF 916) within the control plane can allows other NFs(e.g., NSSF 902, NEF 904, NRF 906, PCF, 908, UDM 910, AF 912, AUSF 914,SMF 918, UPF 924, etc.) that have been authorized, to access itsservices. This representation also includes point-to-point referencepoints between the NFs where necessary (e.g., between AMF 916 and UE,920/(R)AN 922, SMF 918 and UPF 924, (R)AN 922 and UPF 924, UPF 924 anddata network (DN) 926).

In an aspect, the AMF 916 can support termination of non-access stratum(NAS) signaling, NAS ciphering and integrity protection, registrationmanagement, connection management, mobility management, accessauthentication and authorization, security context management, etc. TheSMF 918 can support session management (e.g., session establishment,modification, release, etc.), UE IP address allocation and management,dynamic host configuration protocol (DHCP) functions, termination of NASsignaling related to session management, downlink (DL) datanotification, traffic steering configuration for UPF 924 for propertraffic routing, etc. Further, the UPF 924 can support packet routingand forwarding, packet inspection, QoS handling, can act as externalprotocol data unit (PDU) session point of interconnect to DN 926, andcan be anchor point for intra- and inter-radio access technology (RAT)mobility. A PCF 908 can support unified policy framework, provide policyrules to control plane functions, access subscription information forpolicy decisions in a unified data repository (UDR), etc. Additionally,the AUSF 914 can comprise an authentication server that authenticates UE920.

In an aspect, the UDM 910 can support generation of authentication andkey agreement (AKA) credentials, user identification handling, accessauthorization, subscription management, etc. The AF 912 can supportapplication influence on traffic routing, accessing NEF 904, interactionwith policy framework for policy control, etc. Further, the NEF 904 cansupport exposure of capabilities and events, secure provision ofinformation from external application to 3GPP network, translation ofinternal/external information, etc. Furthermore, the NRF 906 can supportservice discovery function, maintains NF profile and available NFinstances, etc. According to an embodiment, the NSSF 902 can supportselecting of the network slice instances to serve the UE 920 thatregisters via (radio) access network ((R)AN) 922, determining theallowed network slice selection assistance information (NSSAI),determining the AMF (e.g., AMF 916) set to be used to serve the UE, etc.

FIG. 10 illustrates an example system 1000 that depicts a non-roaming 5Gsystem architecture in reference point representation, according to anaspect of the subject disclosure. In one aspect, system 1000 focuses onthe interactions between pairs of network functions defined bypoint-to-point reference point (e.g., N7) between any two networkfunctions. This kind of representation is used when some interactionexists between any two network functions. It is noted that NSSF 902,PCF, 908, UDM 910, AF 912, AUSF 914, AMF 916, SMF 918, UE 920, (R)AN922, UPF 924, and DN 926, can comprise functionality as more fullydescribed herein, for example, as described above with regard to system900. It should be noted that although various aspects and embodimentshave been described herein in the context of 5G networks, the disclosedaspects are not limited to 5G technology and can be applied to otherfuture wireless communication technologies and their evolutions.

Referring now to FIG. 11, there is illustrated a block diagram of acomputer 1102 operable to execute the disclosed communicationarchitecture. In order to provide additional context for various aspectsof the disclosed subject matter, FIG. 11 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 1100 in which the various aspects of thespecification can be implemented. While the specification has beendescribed above in the general context of computer-executableinstructions that can run on one or more computers, those skilled in theart will recognize that the specification also can be implemented incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will note thatthe various methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the specification can also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disk (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or other tangible and/ornon-transitory media which can be used to store desired information.Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, radio frequency (RF),infrared and other wireless media.

With reference again to FIG. 11, the example environment 1100 forimplementing various aspects of the specification comprises a computer1102, the computer 1102 comprising a processing unit 1104, a systemmemory 1106 and a system bus 1108. As an example, the component(s),network(s), application(s) server(s), equipment, system(s),interface(s), gateway(s), controller(s), node(s), engine(s),entity(ies), function(s), center(s), point(s) and/or device(s) (e.g.,self-learning network 102, IoT devices (e.g., 202 ₁-202 _(M)), collectornodes 204 ₁-204 _(M), output engine 206, feedback component 208, network302, SDN orchestration and control component 306, sensor pools 308,access point devices 310 ₁-310 ₂, object nodes 312, non-3GPP cloud 502,AI component 602, data reception component 604, node configurationcomponent 606, NSSF 902, NEF 904, NRF 906, PCF, 908, UDM 910, AF 912,AUSF 914, AMF 916, SMF 918, UE 920, (R)AN 922, UPF 924, and DN 926,etc.) disclosed herein with respect to systems 100-600 and 900-1000 caneach comprise at least a portion of the computer 1102. The system bus1108 couples system components comprising, but not limited to, thesystem memory 1106 to the processing unit 1104. The processing unit 1104can be any of various commercially available processors. Dualmicroprocessors and other multi-processor architectures can also beemployed as the processing unit 1104.

The system bus 1108 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1106comprises read-only memory (ROM) 1110 and random access memory (RAM)1112. A basic input/output system (BIOS) is stored in a non-volatilememory 1110 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1102, such as during startup. The RAM 1112 can also comprise ahigh-speed RAM such as static RAM for caching data.

The computer 1102 further comprises an internal hard disk drive (HDD)1114, which internal hard disk drive 1114 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 1116, (e.g., to read from or write to a removable diskette1118) and an optical disk drive 1120, (e.g., reading a CD-ROM disk 1122or, to read from or write to other high capacity optical media such asthe DVD). The hard disk drive 1114, magnetic disk drive 1116 and opticaldisk drive 1120 can be connected to the system bus 1108 by a hard diskdrive interface 1124, a magnetic disk drive interface 1126 and anoptical drive interface 1128, respectively. The interface 1124 forexternal drive implementations comprises at least one or both ofUniversal Serial Bus (USB) and IEEE 1394 interface technologies. Otherexternal drive connection technologies are within contemplation of thesubject disclosure.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1102, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to a HDD, a removable magnetic diskette, and a removableoptical media such as a CD or DVD, it should be noted by those skilledin the art that other types of storage media which are readable by acomputer, such as zip drives, magnetic cassettes, flash memory cards,solid-state disks (SSD), cartridges, and the like, can also be used inthe example operating environment, and further, that any such storagemedia can contain computer-executable instructions for performing themethods of the specification.

A number of program modules can be stored in the drives and RAM 1112,comprising an operating system 1130, one or more application programs1132, other program modules 1134 and program data 1136. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1112. It is noted that the specification can beimplemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 1102 throughone or more wired/wireless input devices, e.g., a keyboard 1138 and/or apointing device, such as a mouse 1140 or a touchscreen or touchpad (notillustrated). These and other input devices are often connected to theprocessing unit 1104 through an input device interface 1142 that iscoupled to the system bus 1108, but can be connected by otherinterfaces, such as a parallel port, an IEEE 1394 serial port, a gameport, a USB port, an infrared (IR) interface, etc. A monitor 1144 orother type of display device is also connected to the system bus 1108via an interface, such as a video adapter 1146.

The computer 1102 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1148. The remotecomputer(s) 1148 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer1102, although, for purposes of brevity, only a memory/storage device1150 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 1152 and/orlarger networks, e.g., a wide area network (WAN) 1154. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1102 isconnected to the local network 1152 through a wired and/or wirelesscommunication network interface or adapter 1156. The adapter 1156 canfacilitate wired or wireless communication to the LAN 1152, which canalso comprise a wireless access point disposed thereon for communicatingwith the wireless adapter 1156.

When used in a WAN networking environment, the computer 1102 cancomprise a modem 1158, or is connected to a communications server on theWAN 1154, or has other means for establishing communications over theWAN 1154, such as by way of the Internet. The modem 1158, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 1108 via the serial port interface 1142. In a networkedenvironment, program modules depicted relative to the computer 1102, orportions thereof, can be stored in the remote memory/storage device1150. It will be noted that the network connections shown are exampleand other means of establishing a communications link between thecomputers can be used.

The computer 1102 is operable to communicate with any wireless devicesor entities operatively disposed in wireless communication, e.g.,desktop and/or portable computer, server, communications satellite, etc.This comprises at least Wi-Fi and Bluetooth™ wireless technologies orother communication technologies. Thus, the communication can be apredefined structure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity networks use radio technologies called IEEE802.11 (a, b, g, n, etc.) to provide secure, reliable, fast wirelessconnectivity. A Wi-Fi network can be used to connect computers to eachother, to the Internet, and to wired networks (which use IEEE 802.3 orEthernet). Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radiobands, at an 11 Mbps (802.11a) or 54 Mbps (802.11b) data rate, forexample, or with products that contain both bands (dual band), so thenetworks can provide real-world performance similar to the basic 10BaseTwired Ethernet networks used in many offices.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Processors can exploit nano-scale architectures suchas, but not limited to, molecular and quantum-dot based transistors,switches and gates, in order to optimize space usage or enhanceperformance of user equipment. A processor may also be implemented as acombination of computing processing units.

In the subject specification, terms such as “data store,” data storage,”“database,” “cache,” and substantially any other information storagecomponent relevant to operation and functionality of a component, referto “memory components,” or entities embodied in a “memory” or componentscomprising the memory. It will be noted that the memory components, orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can comprise read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), or flash memory. Volatile memory can comprise random accessmemory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such assynchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM),double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SynchlinkDRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, thedisclosed memory components of systems or methods herein are intended tocomprise, without being limited to comprising, these and any othersuitable types of memory.

Referring now to FIG. 12, there is illustrated a schematic block diagramof a computing environment 1200 in accordance with the subjectspecification. The system 1200 comprises one or more client(s) 1202. Theclient(s) 1202 can be hardware and/or software (e.g., threads,processes, computing devices).

The system 1200 also comprises one or more server(s) 1204. The server(s)1204 can also be hardware and/or software (e.g., threads, processes,computing devices). The servers 1204 can house threads to performtransformations by employing the specification, for example. Onepossible communication between a client 1202 and a server 1204 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The data packet may comprise a cookie and/orassociated contextual information, for example. The system 1200comprises a communication framework 1206 (e.g., a global communicationnetwork such as the Internet, cellular network, etc.) that can beemployed to facilitate communications between the client(s) 1202 and theserver(s) 1204.

Communications can be facilitated via a wired (comprising optical fiber)and/or wireless technology. The client(s) 1202 are operatively connectedto one or more client data store(s) 1208 that can be employed to storeinformation local to the client(s) 1202 (e.g., cookie(s) and/orassociated contextual information). Similarly, the server(s) 1204 areoperatively connected to one or more server data store(s) 1210 that canbe employed to store information local to the servers 1204.

What has been described above comprises examples of the presentspecification. It is, of course, not possible to describe everyconceivable combination of components or methods for purposes ofdescribing the present specification, but one of ordinary skill in theart may recognize that many further combinations and permutations of thepresent specification are possible. Accordingly, the presentspecification is intended to embrace all such alterations, modificationsand variations that fall within the spirit and scope of the appendedclaims. Furthermore, to the extent that the term “comprises” is used ineither the detailed description or the claims, such term is intended tobe inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A method, comprising: receiving, by object nodeequipment comprising a processor: sensor data captured via sensorequipment that communicates sensor data via a mobility network, andmetadata about the sensor equipment that has been identified to be ofthreshold likely interest to a customer, according to a definedlikelihood criterion; based on the metadata, classifying, by the objectnode equipment, the sensor data, resulting in classified sensor data;and based on the classified sensor data, validating, by the object nodeequipment, the classified sensor data.
 2. The method of claim 1, whereina function of the object node equipment was instantiated as avirtualized network function in a service slice enabled via the mobilitynetwork for validation of the sensor data, and wherein the virtualizednetwork function was selected for validation based on the metadata. 3.The method of claim 2, wherein the virtualized network functioncomprises a layer used to classify the sensor data received in theservice slice, and wherein subsequent virtualized network functionsinstantiated after the virtualized network function are able to beinstantiated in successive service slices, other than the service slice,enabled via the mobility network.
 4. The method of claim 1, wherein themetadata comprises: first metadata representative of a sensor datacollection capability of the sensor equipment, and second metadatarepresentative of a position of the sensor equipment.
 5. The method ofclaim 1, wherein, in response to determining that the object nodeequipment has not classified the classified sensor data in accordancewith a defined success criterion, the sensor data was reclassified basedon a determined label for the sensor data.
 6. The method of claim 5,wherein a second object node was instantiated to reclassify the sensordata based on the determined label for the data.
 7. The method of claim1, further comprising, employing, by the object node equipment, feedbackdata to update the object node equipment.
 8. The method of claim 1,wherein the sensor equipment comprises Internet of things equipment. 9.A system, comprising: a processor; and a memory that stores executableinstructions that, when executed by the processor, facilitateperformance of operations, comprising: receiving sensor data capturedvia a sensor device that is part of a mobility network and metadataabout the sensor device that was identified to be of threshold likelyinterest to a customer, according to a defined likelihood criterion,based on the metadata, classifying the sensor data, resulting inclassified sensor data, and based on the classified sensor data,validating the classified sensor data.
 10. The system of claim 9,wherein the metadata about the sensor device comprises a location of thesensor device.
 11. The system of claim 9, wherein the system wasinstantiated in a service slice enabled via the mobility network forvalidation of the sensor data, and wherein the system was selected forvalidation based on the metadata.
 12. The system of claim 9, wherein themetadata comprises: first metadata representative of a sensor datacollection capability of the sensor device, and second metadatarepresentative of a position of the sensor device.
 13. The system ofclaim 9, wherein the operations further comprise: receiving anindication that the sensor data was not classified in accordance with adefined success criterion, and reclassifying the sensor data based on adetermined label for the sensor data.
 14. The system of claim 9, whereinthe operations further comprise, based on feedback data provided for theclassified sensor data, updating the system.
 15. The system of claim 9,wherein the sensor device comprises an Internet of things device.
 16. Anon-transitory machine-readable medium, comprising executableinstructions that, when executed by a processor of network equipment,facilitate performance of operations, comprising: receiving sensor datacaptured via an Internet of things device connected via a network andmetadata about the Internet of things device; based on the metadata,classifying the sensor data, resulting in classified sensor data;receiving an indication that the classified sensor data was notclassified in accordance with a defined success criterion, reclassifyingthe sensor data based on a determined label for the sensor data,resulting in reclassified sensor data; and based on the reclassifiedsensor data, validating the reclassified sensor data.
 17. Thenon-transitory machine-readable medium of claim 16, wherein the networkcomprises a mobility network.
 18. The non-transitory machine-readablemedium of claim 16, wherein validating the sensor data comprisesinstantiating a virtualized network function in a service slice enabledvia the network for validation of the sensor data, and wherein thevirtualized network function was selected for validation based on themetadata.
 19. The non-transitory machine-readable medium of claim 16,wherein the metadata comprises: first metadata representative of asensor data collection capability of the Internet of things device, andsecond metadata representative of a position of the Internet of thingsdevice.
 20. The non-transitory machine-readable medium of claim 16,wherein the operations further comprise employing feedback data toupdate the reclassified sensor data.